[EN] Лаборатории

Contract number

075-15-2021-579

Time span of the project

2021-2023

Laboratory's website

since February 2023 Квашнин Александр Геннадьевич

2021 - 2023 Tetko Igor Vladimirovich

Ksenofontov Alexander Andreevich

As of 01.12.2023

Number of staff members

scientific publications

Objects of intellectual property

The project is aimed at the development and the demonstration of a new computational methodology for the design of chemical structures with specific properties augmented by possible chemical reactions. The methodology will be based on new machine learning methods developed at Dr. Tetko's laboratory. This methods will be later expanded, tuned, and tested on several types chemical reactions relying on the outstanding additional the host institution’s experience in chemical synthesis (G. A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences).

Name of the project: Computer-aided synthesis of compounds with given characteristics

Goals of project:

The objectives of the project include:

Artificial intelligence (AI) methods for the production of chemical compounds with desired characteristics.
An automated system for the planning of the synthesis of the proposed compounds on the basis of preliminarily determined sets of chemical reactions.

Scientific results:

Creation of a publicly available web service ChemPredictor (http://chem-predictor.isc-ras.ru/), which allows using machine learning methods to predict the physicochemical and biological properties of a wide range of compounds, as well as the chemical reactions parameters.

At present, the ChemPredictor web service allows the user to perform a complete oligopyrroles retrosynthesis and predict physicochemical and biological properties (maximum absorption wavelength and molar absorption coefficient of dyes of various natures; 11B NMR chemical shift value for BODIPY class compounds; sensory ability of a molecule; melting, decomposition and glass transition temperatures for ionic liquids and mixtures based on them; minimum inhibitory concentration of ionic liquids with respect to S. aureus, E. coli and P. aeruginosa; electrical conductivity, viscosity, density, surface tension, speed of sound for ionic liquids) and reaction parameters (yield of the product of the condensation reaction of pyrrole or dipyrromethane with aldehydes).

The ChemPredictor web service has an intuitive interface that allows it to be used to solve not only scientific problems, but also educational ones.

Education and personnel occupational retraining:

Defense of a dissertation for the degree of Doctor of Science (1);
Defense of dissertations for the degree of candidate of science (5);
Training courses have been developed and implemented: «Chemoinformatics», «Machine learning methods in chemistry».

Cooperation:

Ivanovo State University of Chemical Technology,
Kazan Federal University,
Helmholtz-Center Munich (Germany),
ITMO University,
Skolkovo Institute of Science and Technology,
N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences.

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Makarov D. M., Fadeeva Y. A., Shmukler L. E.

Predictive modeling of physicochemical properties and ionicity of ionic liquids for virtual screening of novel electrolytes // Journal of Molecular Liquids. 2023. Vol. 391. p. 123323;

Ksenofontov A. A., Isaev Y. I., Lukanov M. M., Makarov D. M., Eventova V. A., Khodov I. A., Berezin M. B.

Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning // Physical Chemistry Chemical Physics. 2023. Vol. 25. No. 13. pp. 9472-9481;

Makarov D. M., Lukanov M. M., Rusanov A., Mamardashvili N., Ksenofontov A. A.

Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes // Journal of Computational Science. 2023. Vol. 74. p. 102173;

Bichan N. G., Ovchenkova E. N., Ksenofontov A. A., Mozgova V. A., Gruzdev M. S., Chervonova U. V., Shelaev I. V., Lomova T. N.

Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods // Dyes and Pigments. 2022. Vol. 204. p. 110470;

Ksenofontov A. A., Lukanov M. M., Bocharov P. S.

Ksenofontov A. A., Lukanov M. M., Bocharov P. S., Berezin M. B., Tetko I. V.

Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes? // Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy. 2022. Vol. 279. p. 121442;

Makarov D. M., Fadeeva Y. A., Shmukler L. E., Tetko I. V.

Deep neural network model for highly accurate prediction of BODIPYs absorption // Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy. 2022. Vol. 267. p. 120577;

5-[4′-(1′′,3′′-benzothiazol-2′′-yl)phenyl]-10,15,20-tris(4′-sulfophenyl)porphine as color indicator for visual detection of hydrogen chloride (Patent), 2022;

Syrbu Sergei Aleksandrovich, Lebedeva Natalia Shamilevna, Iurina Elena Sergeevna, Kiselev Aleksei Nikolaevich, Lebedev Mikhail Aleksandrovich, Guseinov SabirSaiidovich

5-[4'-(1'',3''-benzoxazol-2''-yl)phenyl]-10,15,20-tris(4'-sulfophenyl)porphine as fluorescent sensor for detection and quantification of albumina content (Patent), 2022;

Syrbu Sergei Aleksandrovich, Lebedeva Natalia Shamilevna, Iurina Elena Sergeevna, Kiselev Aleksei Nikolaevich, Lebedev Mikhail Aleksandrovich, Skorobogatkina Irina Aleksandrovna,

Database for prediction physical and chemical properties individual molecules and reactions (Certificate of state registration of the database), 2023.

Bichan Natalia Gennadievna, Bocharov Pavel Sergeevich, Ksenofontov Alexander Andreevich, Lukanov Mikhail Mikhailovich, Makarov Dmitry Mikhailovich, Mamardashvili Nugzar Zhoraevich, Mozgova Varvara Arkadievna, Ovchenkova Ekaterina Nikolaevna, Fadeeva Yulia Andreevna, Shmukler Lyudmila Ekramovna,

Hosting organization	Field of studies	City	Invited researcher	Time span of the project
Laboratory «Hybrid modeling and optimization methods in complex systems» Siberian federal University - (SibFU)	Computer and information sciences	Krasnoyarsk	Stanimirović Predrag Stefan Serbia	2022-2024
Laboratory «Research of ultra-low-latency network technologies with ultra-high density based on the extensive use of artificial intelligence for 6G networks» The Bonch-Bruevich Saint Petersburg State University of Telecommunications	Computer and information sciences	St. Petersburg	Abd El-Latif Ahmed Abdelrahim Egypt	2022-2024
Laboratory for Non-linear and Microwave Photonics Ulyanovsk State University - (USU)	Computer and information sciences	Ulyanovsk	Taylor James Roy United Kingdom, Ireland	2021-2023

Computer Synthesis of Chemical Compounds